Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Small is beautiful: discovering the minimal set of unexpected patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Imhotep: an approach to user and device conscious mobile applications
Personal and Ubiquitous Computing
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
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Intelligent environments offer information filled spaces. When trying to navigate among all the offered resources users can be overwhelmed. This problem is increased by the heterogeneous nature of resources in smart environments. Users must choose between a plethora of services, multimedia information, interaction modalities and devices. But at the same time the unique characteristics of smart spaces offers us more opportunities to filter these resources. To help users find the resource that they want and need we have designed a multi-aspect recommendation system that takes into account not only the features of the resource and the user, but also context data like the location and current activity. The developed system is flexible enough to be applied to different resource types and scenarios. In this paper we will describe the identified aspects and how they are merged into a single metric.